Marc Miller – How AI Will Impact Human Resources

Connect with Marc Miller:

• 

Connect with David:

• 
• 
• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Salary.com's Consulting Practice
• Marc S. Miller Associates
• The Death of HR book
• HR Data Labs Season 6 Episode 19
• HR Data Labs Season 6 Episode 17

• “[The successful implementation of AI in HR] is all about the ability for HR to have staff that knows how to tell a story.”
• “At this point, [regulation around AI] has been a lot of fear, uncertainty, and doubt. . . . Let’s do what we can to, if not embrace, at least understand AI.”

Announcer: 0:02

Here's an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record for their discussions into a beaker. Mix thoroughly. And voila, you get the HR Data Labs podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.

 

David Turetsky: 0:46

Hello, and welcome to the HR Data Labs podcast. I'm your host, David Turetsky alongside my co-host, Dwight Brown from Salary.com. Hello, Dwight!

 

Dwight Brown: 0:54

Hello, David, how are you?

 

David Turetsky: 0:55

I'm good, how you doing?

 

Dwight Brown: 0:57

Good!

 

David Turetsky: 0:57

And today we have with us one of my oldest and dearest friends, Marc Miller from Marc S. Miller Associates. Marc, how are you?

 

Marc Miller: 1:05

I am fine. Hello, David. Hi Dwight, it's good to see you. I am thrilled to be back in this encore performance. And for us to catch up on all areas and all topics.

 

David Turetsky: 1:15

And we will!

 

Marc Miller: 1:17

And continue to do our HR Data Labs and to talk about each other's progress and your book and my book and all that stuff. So we're all good.

 

David Turetsky: 1:26

Why don't you? Why don't you bring everybody up to speed in what's going on with your books. Because the last we heard Harriet Rose Jobs had a, she had a problem.

 

Marc Miller: 1:34

When I did away with Harriet Rose Jobs in my previous book called Who Killed Harriet Rose Jobs or HR Jobs, the Death of HR. That was before COVID. Obviously, HR in the workforce was at the center of everything. And HR, therefore was in the center of everything for corporations around the globe to respond to the challenges of COVID. So I knew that I had this book in mind. And I said, Well, I already killed her. So I have to do something about that to bring her back. So I thus created Immortal HR, which is the title of the book. It's called How COVID and Workforce Issues Made HR Jobs Critical and Indispensable, if not immortal. And it's the death and resurrection of Ms. H, Harriet, R, Rose, Jobs. And it's it's it's fabulous. I'm working with, for the first time, a developmental editor, which is fascinating. This is way more than proofreading and all that. It's all about my voice and keeping things consistent and making some decisions about how to put in author's comments and everything. And then in my designer who did design the previous book, so it's going to be a fun read. It's over 300 pages, it's due to be out in fall. And it's all about how she became, she built a new mindset to resurrect herself with collaborations with all kinds of people in the organization, some of whom wanted her dead back in, before COVID, 2018, 2019. And then all of a sudden Connie Vid, COVID, appeared along with Pam Demic and all the people that she had to deal with said oh my god, what do we do and HR has the data about their workforce. So she created working groups and deliverables and talking about everything that had to be done and embraced a new mindset where she was no longer considered an obstacle and all that stuff and she was considered immortal and critical and needed because she did, she was. She built all kinds of things, HR did, with along with everybody like Mr. Bennie Fits and Cher Holders and Arturo Intelligente and Ana Lytics, which we're gonna get to.

 

David Turetsky: 3:51

I love that one. She's one of my best friends.

 

Marc Miller: 3:54

Yeah, she is a great friend, Ana Lytics, Hyman Brid, Sir Work From Home, all of these people are in, are in the book, contributing and collaborating against Connie Vid with Harriet Rose Job.

 

David Turetsky: 4:09

Sure. And for those of you don't know, Marc, Marc is a brilliant HR technologist and HR consultant. He's been one of the people I've relied on probably over a couple of decades at least. And if you haven't heard him speak, you have to go to a conference where he's speaking, or if you're in the New York metropolitan area, and you're interested in taking classes, he's also an adjunct professor at where's that small, small college what was the name of it?

 

Marc Miller: 4:37

New Rochelle University? Oh! New York University!

 

David Turetsky: 4:42

Yeah NYU.

 

Marc Miller: 4:43

NYU, and Manhattan college as well, but yes. And it's fascinating.

 

David Turetsky: 4:48

Yeah, he's a brilliant guy, and he gets to help educate the next generation of workforce about the importance of HR and so if you haven't heard him if you, if you're interested, read his book. Listen to his podcast and other podcasts as well as pick up his book, please do.

 

Marc Miller: 5:04

The new one won't be available for a couple of months but

 

David Turetsky: 5:08

Sure but pick up the current one!

 

Marc Miller: 5:09

Yeah, the current one is still out there.

 

David Turetsky: 5:12

And so as all of you who've listened and followed the podcast in the past, there has to be this one step we take, which is the one fun thing that no one knows about Marc S. Miller. Give us give us a new one, Marc. What's the one fun thing that no one knows about you?

 

Marc Miller: 5:29

I like whimsy a lot. And like your office, which I've seen, my office is full of weird stuff that just attracts me. Some of it is even picked up at HR conferences and, and all of what's called tchotchkes that I pick up, but but I like strange things. I've got a Humpty Dumpty puppet behind me.

 

David Turetsky: 5:53

Was it sitting on the wall, Marc?

 

Marc Miller: 5:54

No, he's sitting on the couch.

 

Dwight Brown: 5:57

He just he just had a big fall.

 

Marc Miller: 5:59

It wasn't Humpty Dumpty, I'm sorry. It's Howdy Doody.

 

David Turetsky: 6:03

Oh, Howdy Doody? Okay.

 

Dwight Brown: 6:05

Oh, Howdy Doody okay.

 

David Turetsky: 6:07

Nobody puts Howdy Doody up on a wall.

 

Marc Miller: 6:08

No, I like that. And I been eating a lot more Mediterranean food recently. And I really enjoy Persian food, Mediterranean Greek food. And it's plentiful here in Westchester County outside of New York. I think all my doctors will be very happy with my cholesterol levels.

 

David Turetsky: 6:27

Yeah olive oil is great for you.

 

Marc Miller: 6:30

Yes!

 

David Turetsky: 6:31

No. And falafel. Falafel and hummus. Well, now we know much more about you. So now we're gonna turn our attention to the topic.

 

Marc Miller: 6:39

Yes.

 

David Turetsky: 6:40

Our topic for today is going to be artificial intelligence and its impact on HR.

 

Marc Miller: 6:44

Yes.

 

David Turetsky: 6:45

And so we're gonna get into a couple of, of discussions around what it is and how it's emerged. So Marc, our first question is, where has artificial intelligence been? Because it's not brand new. It's been around for a little bit. In your experience, what is, what is artificial intelligence? How has it affected HR in the past?

 

Marc Miller: 7:13

Very good question. Obviously, it's been around. I mean, if you read some of the more technical, scientific oriented data geek type of papers, they'll say it's around for 30, 40 years, believe it or not, in their own work and research labs and everything. But it really hit the public domain, just back in October, when the New York Times published all kinds of stuff showing prompts to Open AI's ChatGPT. And that got the public consciousness all a twitter, so to speak, no pun intended. And it shook up everything. And I'm involved on both the corporate side and academia. And immediately out of the box, everybody was saying, Oh, my God, the students are going to use it to write essays and cheat!

 

Dwight Brown: 7:58

Oh, yeah.

 

David Turetsky: 7:58

Yeah, we heard that for a while.

 

Marc Miller: 7:59

But in HR, when we're dealing with analytics and predictive and prescriptive analytics, those terms had been around for years. Probably the one that I think resonated most with me is back in the day when UKG was not UKG and it was ultimate software before they merged with Kronos. They had purchased a company and put into their system, something called sentiment analysis. They were able to say in their marketing poop, as an analyst, I was in front of them a number of times, that using algorithms that were able to watch freeform English words, typed by employees sitting on a company device, and determine their happiness or unhappiness, their emotional state, their sentiment. And they were able to actually make predictions about an employee based on their usage of words in freeform memos. And then use that to plot the classical nine box model. And then add to the nine box model saying, Oh, David Turetsky, he's a key employee, he's going to be your high performance, high potential. He's in the upper right of nine boxes for its potential versus performance, low, medium, and high. If you can picture it.

 

David Turetsky: 9:23

Right.

 

Marc Miller: 9:23

And then next to your name, and anybody else in that group of the top box maybe the key, the key employees, or the stars, they would actually be able to create a graphic bubble that shows the size or the propensity for you to leave the company called flight risk. And they do that based on your unhappiness or happiness factor and other minions that they built in. Which is which is really exciting back then, this is 5, 6, 7, 8 years ago as we were talking and then they moved forward with it and all the vendors now are using it. Which brings me, as you know, like you know me well enough, David, that when something hits my head, I just keep talking as part of my, my. So it brings me to one of the critical issues now about bias infecting HR.

 

David Turetsky: 10:15

Right.

 

Marc Miller: 10:15

And especially in recruiting. And most people, and you and I know this and Dwight of course knows this, as he's coordinated all this and has been involved with HR Data Labs, recruiting seems to be the one area of the world and HR management, in my view, that has the most to gain and also the most vulnerabilities in using built in algorithms. The most to gain is yes, of course, a recruiter can use this to build job descriptions, to match resumes with job openings, and all of that. But there's bias in what was built, potentially in recruiting, because whoever built the module that looks at resumes, is looking at key capabilities to fill a position. And here's what happened and it's still being litigated today. There is a case, Mobley versus Workday, that is in the court system as we are speaking. In fact, any day now, there's going to be a motion to dismiss by the Workday lawyers, of a gentleman by the name of Mobley, I forgot his first name, a gentleman of a certain age, middle aged, of a certain race and gender, not white, decided that, he had applied to 60 companies and was rejected for all 60, and when he did the research, he found that all 60 use Workday's applicant tracking module. So he said, I'm not going to sue all these extra, these companies, I'm going right to the source. And I'd put a lawsuit together accusing Workday of bias against people of color and age, in their applicant tracking recruiting module that they sell around the globe. Workday said, No, we stand by our functionality. And then and now they're still going to court. And Workday said, We're not an agency, we, you're, you're suing us inappropriately because we deliver software, and the company that purchases our module, they have the ability to customize it, and use the algorithms that we use, and they're unbiased, we've tested them, and we have the proof. So it's still a fight. And it's, Workday moved to dismiss and this guy said no, and they fighting each other. But this is one of the first times where a company, like a software vendor in our industry, HR Tech, has been targeted by a large lawsuit about bias, which is one of the biggest vulnerabilities of using algorithms or generative AI, whether it's Chat GPT, or Bard, or any one of these other things.

 

Dwight Brown: 12:59

And it makes sense. I mean, you look at who, who programs these things and what is their perspective? And you see it happen over and over again, not just in the recruiting space, but but other elements of HR.

 

Marc Miller: 13:12

It makes sense logically, if a company says, Well, we're looking for salespeople, let's look at the history of salespeople for the last four decades. Guess who they are? They're white, middle aged men, whose capabilities have proven to be good salespeople. So they somehow even unconscious bias creeps into whoever's programming their module. And that's what the root of the problem is. There's so many issues.

 

David Turetsky: 13:36

Well, yeah. But Marc, we also have been saying since day one of this podcast, which is now three years ago, we've been saying that HR data is flawed. And in fact, that was our first podcast, actually. And if you're trying to make decisions based on HR data, and HR data is flawed. By its very nature, the algorithms that you're creating are going to be flawed indeed, they're going to be flawed. And so you know, I'm looking right now at the docket for this litigation. And last update was July 4th. And so it's it's continuing. I actually think it was made a class action suit.

 

Marc Miller: 14:14

Right.

 

David Turetsky: 14:14

And so,

 

Marc Miller: 14:16

Excellent.

 

David Turetsky: 14:16

you know, the class will be pretty wide. For people who've been rejected by AI, without even you know, getting a pass at the, by humans. All of us have had that. We've all applied to jobs and gotten emails back within minutes of hitting, submit, gotten rejected. I don't think it has to necessarily be that you're African American, or that you're over 40, that you're male or whatever. I think all of us have had that situation. And the other thing that I want to bring up in what you were saying before is that we've always struggled with the ability for for HR to be able to use data, not just because it's flawed, but because because it's hard for HR to prove its value by creating that algorithm. Meaning that for HR to connect with the business and make the investment in whatever, whether it's artificial intelligence or simple regression analyses, you have to explain it to your business and you have to explain your business leaders. And HR has never really been very, what word fertile? With analytic types other than in comp, usually, because comp has been very analytical for decades.

 

Announcer: 15:34

Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com. Now, back to the show.

 

David Turetsky: 15:45

So I guess the question goes back to you, you know, as an HR technologist, is it the technology is getting more consumer oriented? Or is the technology getting better? In order for HR to start to consume some of these new technologies and some of these new algorithms?

 

Marc Miller: 16:04

I have a simple answer to that. I think the fault lies, the fact that HR, and you're correct in saying HR has struggled, but it's all about the ability for HR to have staff that knows how to tell a story. Just like your HR data, the Data Doodles.

 

David Turetsky: 16:21

Shameless plug.

 

Marc Miller: 16:21

The ability for data visualization, and tying a holistic view with supporting metrics and analytics to the senior level people that make actionable insights, that take the actionable insights and make strategy initiatives happen, and then give HR its due in pointing out trends that will be meaningful and impact the company's mission, goals and vision. But so, one, HR has to be privy to that, and therefore you need a good CHRO or whatever leader that you want that's up there at the higher levels, knowing that some company might want to be generating a new product in a year or two. And they need salespeople to know the feature functions of an electronic vehicle like an E vehicle. And it takes six months to a year for good car salesmen to know it. So we need to look at their turnover of car people in the showrooms all around the world and say, Guess what, we've got a new EV model coming in January of 2025. We don't have a lot of time, and it takes time. So let's start recruiting now instead of waiting until it's too late. So I mean, these are all simple, simplified discussion of the fact that HR has the data, has the ability to do anything from the lower level ad hoc reports to predictive analytics to prescriptive analytics. And they need the right people on their staff reporting into the function of HR, who I just saw in Time Magazine did a whole article about prompt engineers, David and Dwight, and those jobs are$300,000 now, where somebody knows how to build appropriate prompts to get artificial intelligence to learn, and generate effective data.

 

David Turetsky: 18:12

But Marc, we've seen this happen before. I mean, there have been cycles, like when dotnet was first, you know, first introduced or even in the 19, like late 1990s, when we were worried about y2k, and mainframe programmers who had moved on to other web based technologies. We were recruiting them for high six figures, to be able to come back and make sure that the core code that they had developed for banks for, you know, flight systems for other things weren't going to fail on, you know, not just January 1 2000. But any time there was that, that crazy combination of of dates, that you know, if as long as it wasn't 1999, or beyond, or 99, because we didn't care about the four year code when they were developing technologies. So that's happened before, right? And we've seen, like when I was in financial services Marc, and we were starting to talk about derivative products for the first time in the 90s. And options were not necessarily new, but the ability to then be able to account for options as a traded product on the open market, and then be able to do some really cool derivative products from it. Well, we needed to then find accountants who understood what the derivative product was, to be able to do a lot of the forensic science behind unwinding what this really cool thing was, so okay, then you had to hire an accountant who had derivative knowledge. They didn't exist! So you're, you were basically trying to reskill people who had other knowledge to be able to bring that in. That would have been really cool if a predictive model would have been able to say, hey, you know, in six months, our accounting group is going to need to have these kinds of skills because we've just hired a bunch of people, we've just transitioned into a new product line.

 

Marc Miller: 20:04

Yep, absolutely. But what stepped in this time that's different. One is the visibility of things like Chat GPT, the reading public being inundated with stuff from newspapers, magazines, and internet driven, newsletters and all the stuff you get from LinkedIn, even if you're not in the function of HR, you're being inundated with organizations offering become a chat prompt engineer, understand the basics of Bard versus Chat GPT. Understanding, understand all of this, and academia, well, they they had a fit when it first came out. And now they're saying, Oh, we're going to create a major or a minor within a degree, an MBA or a Master of Science on generative AI and using it in all industries. Now, clearly, in the world of HR, recruiting gets it and onboarding gets it and administrative tasks gets it very nicely, that it's going to be helpful, assuming we could control and understand if there's built in bias and a way to detect it and eliminate it. And that's the problem that's facing Workday right now. But certainly a Chat GPT or an AI type of driven virtual assistant is already doable, performance reviews, employee pulse surveys, everything can be blended with artificial intelligence, today as we're speaking, it's easy. And the other thing you read things like from Josh Bersin, the Bersin company, the vendors are integrating AI into their system, from the ground up. They're not just buying a company that offers it and trying to build it into their HRIS. They're building new, foundational architecture that relies on generative AI for every workflow that you and I grew up on in terms of hire, fire, termination, leave of absence, all of that. So it's, it's going to have a major impact in HR. And there was another thing that I wanted to say, there was an article written because let me see if I could find a source while I'm looking. It hit me with such force, that I added a postscript to my book, which was sitting almost completed. And the postscript said, There's troubled waters ahead. The troubled waters come from a quote from the Stanford Institute of Human Centered AI, who never heard of such a thing just two years ago. But there was a woman in there that was interviewed at the New York Times on August 24, just a few weeks ago, and it's already in my book, here's what she said, this was a woman at the Stanford Institute for Human Centered AI. And she said, to be brutally honest, we had a hierarchy of things that technology could do. And we felt comfortable saying that things like creative work, professional work, emotional intelligence, would be hard for machines to ever do. Now, that's all been up ended. Holy cow! What we used to think was, oh, nobody's ever gonna replace me! I've got 20 years of experience in how to implement an HR technology product, that's not the case, according to this particular person and their research. So I put that in the last page of my book talking about for Harriet Rose Job and her colleagues is their troubled water on the horizon? Because Arturo Intelligente, Artificial Intelligence she collaborated with and then being the crazy person that I am I ended the book with these three words, et tu Arturo question mark.

 

David Turetsky: 23:56

There you go.

 

Marc Miller: 23:57

Right? It's beautiful, because that's what's going to happen, things that we thought were safe, we're in management, I don't need to worry about artificial intelligence taking us over. And I think that's going to change.

 

Dwight Brown: 24:10

That definitely is an argument that you hear over and over again, and that we're going to, we're going to have obsolescence of people. And the reality is, I think it's like any other, any other sort of technology that we've implemented, that what it's going to do is just shift where those people are.

 

Marc Miller: 24:28

That's right.

 

Dwight Brown: 24:28

Because you got to have people that keep this up, you've got to have, you've always got to have people on the back end of things.

 

Marc Miller: 24:34

You know, it's funny, it reminds me of that classical joke about the Office of the Future has has one employee, it has robots, and a dog to keep the security and then one human to feed the dog. And that was it.

 

David Turetsky: 24:51

Well, there are technologies that enable that as well right now. Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about This? Well, you're in luck, we have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast or whatever is on your mind. Go to Salary.com/HRDLconsulting to schedule your FREE 30 minute call today. Let's move to the third question, which is, what does HR have to worry about with this? And I think you started going that way, which is that there are lots of workflow tasks that can be automated, we get that, it's totally sure. Absolutely. And even with the concept of discretion, and being able to add in flexibility into that, sure, but machines can get that. But again, what we're talking about is we're talking about data. And we're talking about HR data, which most of the time is crap. And so one of the ways in which, and we had this conversation a long time ago, we had people talk about using artificial intelligence to query HR data, and to find patterns in HR data that looked wrong, which

 

Marc Miller: 26:08

I like it.

 

David Turetsky: 26:09

yeah. Okay. If the data can self heal the, Oh, yeah, that's phenomenal.

 

Marc Miller: 26:12

Yeah.

 

David Turetsky: 26:13

If the data can help self heal, by using technology to look at it, and to say, this is inconsistent or doesn't look right, then, you know, maybe there is a way for artificial intelligence, to not replace HR, but to enhance it to the extent at which like we're looking or we're seeing in supermarkets. You don't need as many checkout aisles, because now self service is easier, automations made it easier.

 

Marc Miller: 26:39

Frictionless!

 

David Turetsky: 26:40

Right, you're right. But you still need to Dwight's point, you still need that person who's standing there who can change the receipt feeder. Who can unjam things, who can override prices, because they're wrong, because the data's wrong. So HR will probably still be there in a more limited capacity at some stage. Right? But it will still be there.

 

Marc Miller: 27:02

Absolutely.

 

David Turetsky: 27:03

Even if it's just to program the machines.

 

Marc Miller: 27:05

Yeah, there's always got to be human empathy, human intelligence, human emotion, that is not available to any generative language. Because

 

David Turetsky: 27:16

Not yet!

 

Marc Miller: 27:16

Not yet, well.

 

David Turetsky: 27:18

Yeah. If you see that Lieutenant Data from Star Trek, he developed a heart. He developed the ability to have emotions at the later stages of his, quote unquote life, or the series.

 

Marc Miller: 27:32

True.

 

David Turetsky: 27:33

So it's possible.

 

Marc Miller: 27:35

Right. Is it feasible? Is it workable? Listen, there's software that does career journeys, which I've always been impressed with, from vendors like Fuel50, Culture Amp and a few others, they could put down, an end user could be the janitor in a large corporation, or a cafeteria worker, let's go there, who says you know, I love this company. I'd like to see if I could become the CHRO. Right now I'm working in the cafeteria, they can input that into the software that uses generative AI and algorithms and come up with a 20 year journey, it will take 20 years, maybe by the software telling them that and these 35 steps along the way to give this person a almost like a project plan. But it's feasible and doable. And the software doesn't stop this person saying, Oh, you'll never become the CHRO, you're working in a cafeteria! But that's what is interesting. Now HR has to oversite that and allow that to happen if this person does want to unblock.

 

David Turetsky: 28:37

Well, I wouldn't say allow, I'd say enable that to happen.

 

Marc Miller: 28:40

Enable, that's a better word.

 

David Turetsky: 28:42

I've been in organizations where you know, we HRs encourage those things to happen.

 

Marc Miller: 28:46

Absolutely.

 

David Turetsky: 28:47

Which is better than saying, yeah, no, you're, you stay in that job. We want you in that job forever, which other organizations have done.

 

Marc Miller: 28:55

Well, those other organizations have low, low, low, low levels of employee engagement and high turnover for sure. So we don't care. Doing my research for the book, I found some discussion about something called not Chatbot. But woebot, w o e b o t, like, oh, woe is me. And if this was created in 2017, and it helped treat mental health, stress and suicide, using feedback loops, it can identify a user who is considering suicide by listening to how they talk in podcasts and how they write, similar to the sentiment analysis, in memos. And then they could intervene and as with a human person contact and have a robot intervene and say, Hey, Marc, your sound very much vulnerable. Is everything okay? What can we do to help you? And things like that. But there's a bot out there called woebot as opposed to chatbot or robot. It's pretty interesting.

 

David Turetsky: 29:53

When you said they listen to their podcasts, you pointing that at us? I mean

 

Marc Miller: 29:58

You look a little sad.

 

Dwight Brown: 29:59

Yeah I wonder what the AI is saying about us?

 

David Turetsky: 30:02

Yeah, no, I'm not sad, I am very happy. I'm very happy because I'm talking to my two best friends about something I love. So that's that. The book is yet to be written on this, right? Yes. You know, there's there's your book which discusses it and it's a really fun way of introducing this. But at some point, what we're going to start to see is the consumer applications of artificial intelligence get so good and so imbued. Yeah, there's Siri and yeah, there's Hey, Google, and yeah, there's Alexa. But even there have been discussions about actually them going away, because they've not achieved the commercial success that's necessary for the investment that's been made. And other tools that are available commercially, are actually eclipsing them right now. So what we're gonna see is some cannibalism of these technologies, where the current set are superseded, you know, just like BlackBerry was superseded by Apple, in its pursuit of the business customer on their mobile platforms. We're gonna have to see where, you know, it's gonna, they're gonna see iteration after iteration of this, and maybe Chat GPT is the platform, maybe not.

 

Marc Miller: 31:20

One of the things that we're not mentioning overtly in this podcast is the concept of regulation, and how it could,

 

David Turetsky: 31:27

Of course.

 

Marc Miller: 31:29

How it can arise, and there's not a lot of choices, it's either self regulation, have a federal government in the United States or around the world, having agencies, third party agencies, or not federal agencies or corporate boards, getting people together, like Open AI, and Microsoft, and Google and Apple and all that. I mean, the regulation aspect of this is yet to be seen, the federal government is having a couple of congressional hearings and Senate panels. But nothing's. Europe is far ahead of America as far as worrying about privacy and intrusion than we are. And we don't know how all this is going to be managed. And

 

David Turetsky: 32:13

But it's, but at this point, it's been a lot of FUD, which is fear. It's fear, uncertainty and doubt, right?

 

Marc Miller: 32:19

That's exactly right.

 

David Turetsky: 32:20

The regulation right now is coming at it not from a enablement but from a repression perspective. And you know what happens when that happens? Right? People go underground, or they find ways around it, which is not what we should be encouraging at this point.

 

Marc Miller: 32:34

Right.

 

David Turetsky: 32:34

So to me, Marc, you know, and I'm hoping that our legislators are listening or paying attention, hopefully. Let's do what we can to, if not embrace, least understand what this stuff is, in order to be able to educate the future people around it. You know, as you mentioned before, in education in academia right now. So that we can align, whether the future is tomorrow or the future is next year. You know, what, what will we see?

 

Marc Miller: 33:08

One of the things that's interesting, every university now has created template language to put into every course's syllabus about what students can expect, and it's at the professor's discretion at some of the universities that I'm aware of saying, Will you allow your students to use Chat GPT? And if they do, or anything like it, what do you expect to get back from them and how do you control it? And that, in alone in itself on series of webinars offered by the management of these universities on okay, what's the best way to teach to allow it or to avoid it? In my my view, from my classes on HRM, and HR technology, I'm telling my students to answer my short essay questions. And we'll read the key articles that I'm giving them both classical and recent, about the world of HR, like, it starts with why I hate, Why We Hate HR written in 2005, Fast Company Magazine, my students half them weren't born in 2005. It's funny, but I'm asking them that this as the first article in my course. And then I give them other things to read. And I have to decide, when they give me back their assignment, what they thought about these articles, they could easily put them into a prompt and give Chat GPT the three articles and write up an essay, ask it to write a summary and hand it back to me. So I'm saying no, you can't do that. I'm not an idiot. I've done that already. So you have to get the prompt or tell me what you did and attach it to your assignment. And I know English is not normally the native language and some of my students. So it's a difficult challenge and all these universities around the world are focusing on it. And and of course, what the universities are focusing on is a bigger picture than HR management focusing on their world, the realm of HR, which is growing and growing, because the workforce is so diverse.

 

David Turetsky: 35:05

But Marc, you know, one of the funny things is that I remember back to when I was a little kid, and the Casio watches, those, you know, the Casio watches that had the little calculator on them?

 

Marc Miller: 35:14

Yeah!

 

David Turetsky: 35:14

They were brand new. And there were some kids who were actually bringing those into class. And they were scolded for it. And they were told they can't use that during tests. Well, you know, fast forward to years later, my kid is actually using as a primary tool in his arsenal at school, a Chromebook every single day, you know, to do work. Okay, well, the world has changed, he's never going to need to have the Casio watch. He's got, he already has an Apple Watch. But calculators are, you know, they used to be the thing. And listen, when we were growing up there, there was a time when we didn't have calculators, right? Boltzmann brain and Texas Instruments, you know those were new.

 

Marc Miller: 35:55

Slide rules, all of that. The funniest thing in my classes this semester, and last semester, and even before that, when I asked the students to fill out a seating chart, half of them do not, do not have a pen or a pencil.

 

David Turetsky: 36:10

Yes.

 

Marc Miller: 36:11

You have master's degrees

 

David Turetsky: 36:12

They're using iPads!

 

Marc Miller: 36:14

Right. They have to borrow my pen and pencil to write their name in a box, so I know where they're sitting. It just shook me to my core, I couldn't believe it, I laughed my rear end off in front of the class. Saying, kids bring a pencil to a class, at least that and they sit there and they don't take notes. They're either recording me or they're maybe doing something with a pen or one of those marker pens. It's amazing how things change. You're right.

 

David Turetsky: 36:44

It has, and that's why the world is different.

 

Marc Miller: 36:46

Yeah. And that's why older people, like maybe me, are just doing what we can to educate, pass along our knowledge and hope for the best. And podcasts like HR Data Labs, and you and Dwight are doing a service just for that reason alone, getting people to talk. And hopefully your listeners will pick up a kernel or two and use it.

 

David Turetsky: 37:12

Exactly. And so Marc on that basis, thank you so much for imparting your wisdom and knowledge. We really appreciate it.

 

Marc Miller: 37:27

Thank you very much. It's always a joy to see you and Dwight Yeah.

 

David Turetsky: 37:31

And Dwight, thank you.

 

Dwight Brown: 37:32

Thank you. And thanks for being with us today, Marc. It has been fascinating.

 

Marc Miller: 37:36

It's always fun.

 

David Turetsky: 37:37

And thank you all for listening, take care and stay safe.

 

Announcer: 37:40

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